公开小鼠骨微CT数据集与6个自动生长板检测方案
MiceBoneChallenge: Micro-CT public dataset and six solutions for automatic growth plate detection in micro-CT mice bone scans
- 构建83只小鼠的3D微CT标注数据集，用于生长板定位
- 六种方案平均误差仅1.91±0.87层，达到临床可用水平
- 适合医学影像分析、生物制药领域研究者使用
在前临床药物开发中，对啮齿类动物微CT扫描中的骨骼变化进行检测与量化是一项常见任务，但传统方法依赖人工，耗时且存在观察者间与观察者内差异。2024年，匿名公司组织内部挑战赛，旨在开发自动骨骼量化模型。我们准备并标注了来自83只小鼠的高质量3D μCT骨扫描数据集。该挑战吸引了全球超过80名人工智能科学家组成23支队伍参赛。参赛者需识别骨骼生长发生的平面，这对实现松质骨的全自动分割至关重要。最终，六种计算机视觉解决方案被成功开发，可在测试集上实现相对于真实标注的平均绝对误差1.91±0.87层，达到放射科医生实际应用可接受的精度。所发布的标注数据集、六种解决方案及源代码将公开共享，为研究人员提供模型开发与基准测试的基础。
原文摘要 · Abstract (English)
Detecting and quantifying bone changes in micro-CT scans of rodents is a common task in preclinical drug development studies. However, this task is manual, time-consuming and subject to inter- and intra-observer variability. In 2024, Anonymous Company organized an internal challenge to develop models for automatic bone quantification. We prepared and annotated a high-quality dataset of 3D $μ$CT bone scans from $83$ mice. The challenge attracted over $80$ AI scientists from around the globe who formed $23$ teams. The participants were tasked with developing a solution to identify the plane where the bone growth happens, which is essential for fully automatic segmentation of trabecular bone. As a result, six computer vision solutions were developed that can accurately identify the location of the growth plate plane. The solutions achieved the mean absolute error of $1.91\pm0.87$ planes from the ground truth on the test set, an accuracy level acceptable for practical use by a radiologist. The annotated 3D scans dataset along with the six solutions and source code, is being made public, providing researchers with opportunities to develop and benchmark their own approaches. The code, trained models, and the data will be shared.
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